Stock index price prediction method for quantum neural network

Abstract

The invention relates to a stock index price prediction method of a quantum neural network. The method is based on a main ensemble empirical mode decomposition algorithm, namely a PEEMD algorithm, and comprises a data input module, a data preprocessing module, a data conversion module, a data training and prediction module and a data reconstruction module. The data input module is used for acquiring latest transaction data of the stock index. The data preprocessing module is used for decomposing data, the data conversion module is used for converting original data into quantum state data, the data training and prediction module is used for carrying out training prediction on the quantum state data, and the data reconstruction module is used for reconstructing a prediction result of the data. The method comprises the following steps: firstly, preprocessing original data by using a PEEMD algorithm; decomposing non-stationary time sequence data into a plurality of approximate stationary data with different frequencies, removing high-frequency components in the data, only low and medium frequency components being subjected to simulation prediction through a quantum neural network, and finally reconstructing simulation results to obtain a final prediction result, so that the prediction performance of the model is effectively improved.

Description

technical field [0001] The invention relates to the technical field of stock index price forecasting methods and the field of algorithm application, in particular to a quantum neural network stock index price forecasting method. Background technique [0002] Unlike general linear systems, the stock market has strong regularity. It often has the characteristics of nonlinearity, volatility and unpredictability. Not only that, the trend of the stock market will also be affected by the economic environment, people's expectations, etc. factors. [0003] For the nonlinear and volatile complex system of the stock market, building a model to predict the stock price is essentially building a mathematical model to simulate the nonlinear relationship between input and output parameters. [0004] There is a positive correlation between economic growth and financial market development. The stock market promotes economic growth, which promotes the circulation and transaction of other idl...

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Problems solved by technology

[0007] The invention provides a quantum neural network stock index price prediction method aimed at solving the problems of low prediction accuracy of trend direction and low model training efficiency when only using historical market transaction information to predict stock index prices

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